Machine Learning (ML) – Think “training” an AI to make predictions. (Used in trading bots, recommendations, etc.)Generative AI

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Machine Learning (ML) – Think “training” an AI to make predictions. (Used in trading bots, recommendations, etc.)Generative AI – Like ChatG

From 25 € /h

⚙️ Step 1: Understand What AI Actually Is
AI isn’t magic — it’s just pattern recognition on steroids. It learns from data, spots trends faster than humans, and automates decisions.
There are three main flavors you’ll be dealing with:
Machine Learning (ML) – Think “training” an AI to make predictions. (Used in trading bots, recommendations, etc.)
Generative AI – Like ChatGPT or Midjourney; it creates text, code, or images.
Automation AI – Uses rules + ML to perform actions automatically (Zapier, n8n, AutoGPT, etc.).
💸 Step 2: Using AI in Trading
AI trading isn’t new — hedge funds have been doing it for decades. But now you can do it too.
Here’s the stack you’d look at:
Data Source – Pull data from APIs (like Binance, Alpha Vantage, or Polygon.io).
Model – Use AI to find signals. Common tools:
Python + TensorFlow/PyTorch → train models that predict price movement.
GPTs → analyze news sentiment.
Execution – Automate trades using APIs (like Binance or Alpaca).
Testing – Backtest with historical data before going live.
If you want plug-and-play options, try:
Koyfin, TrendSpider, or Trade-Ideas for AI-driven analysis.
Composer or Stoic.ai if you want “autopilot” portfolios.
(Pro tip: the real edge isn’t in fancy models — it’s in feeding your AI clean, unique data.)
📱 Step 3: Using AI to Build Apps
This is where things get fun. You don’t even need to code hardcore anymore.
No-code builders: Glide, Bubble, or Adalo → use AI APIs like OpenAI, HuggingFace, or Replicate to add brains.
AI dev tools: Replit Ghostwriter, GitHub Copilot, or ChatGPT’s code interpreter to write real code fast.
Frameworks (if coding):
Python/Flask or FastAPI for backend.
React or Next.js for frontend.
Firebase or Supabase for hosting & data.
Hook it all together with AI APIs (like OpenAI, Anthropic, or Stability AI).
🚀 Step 4: Learn by Doing (Don’t Just Watch YouTube)
The fastest way to master AI is to build real stuff:
Create a sentiment analyzer that trades crypto based on tweets.
Build an AI app that generates marketing copy for clients.
Automate your own work — make an AI that handles your emails or schedules.
📚 Step 5: Resources That’ll Level You Up
Learn ML basics: fast.ai or Google’s ML Crash Course.
Build AI apps: freeCodeCamp or YouTube’s “Code with Tomi” / “Nicholas Renotte.”
Trading AI: Check out “Algorithmic Trading with Python” by QuantInsti.
No-code AI apps: Buildspace or FlowiseAI.

Extra information

laptap

Location

Online from Belgium

Age

Teenagers (13-17 years old)

Adults (18-64 years old)

Seniors (65+ years old)

Student level

Beginner

Intermediate

Advanced

Duration

45 minutes

60 minutes

90 minutes

The class is taught in

Dutch

English

Persian

Skills

Digital arts

Computer programming

Availability of a typical week

(GMT -05:00)

New York

Online via webcam

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

Matías

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As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others.

I'm used to working with people of different ages. I believe in the importance of segmenting learning, visualizing progress, setting concrete goals and practicing regularly.

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I am waiting for you to start this adventure.

Gaëlle

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While adults are still debating whether kids should use AI, they are already using it.
The question isn't "should they?" it's "how do we do it intelligently?"

In this course, your child will discover:
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Format: Online | 60–90 min sessions | Flexible, adapted to their age and pace

For curious kids asking "How does ChatGPT actually know things?"

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What You'll Learn:
ChatGPT API Integration: Add AI chat, content generation, or text analysis to any web app
Prompt Engineering: Write effective prompts that give consistent, quality results
Production Implementation: Rate limiting, cost management, error handling, security
Real-World Deployment: Deploy AI features to AWS with proper monitoring and logging

Technologies:
OpenAI API (ChatGPT-4)
React/Next.js frontend integration
Node.js/NestJS backend
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AI chatbot for your website (customer support, FAQ assistant)
Content generation tool (blog posts, product descriptions, social media)
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Working code you can deploy immediately
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No AI experience needed - just basic JavaScript/React knowledge.

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The 3-5 priority AI concepts the student should master.
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Hello and Welcome!

I am a certified corporate programming trainer specializing in Python and Data Analysis, with over a decade of experience in tutoring and software development. My goal is to make programming approachable and engaging by helping learners build a strong foundation and grow into confident, independent coders.

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Lambda functions and list comprehensions
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This course introduces essential tools for data manipulation and analysis using Python libraries.
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What's in it?

PART I

1. Artistic & visual language

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This section is optional and may be skipped for students with prior digital experience. In such cases, the focus will be on refining and strengthening what has already been acquired.

IMPORTANT!

All sections in Part I (artistic practice, visual language, world-building, technical and digital foundations) are approached according to the student’s existing level, from beginner to advanced. If foundational skills are already present, these areas will not be repeated mechanically; instead, the focus will shift toward portfolio development, conceptual depth, and refinement of existing work.

PART II

Portfolio development & mentorship

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